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<div class="title">graph_lenet.cpp</div>  </div>
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<a href="graph__lenet_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2017-2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="graph_8h.xhtml">arm_compute/graph.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="preprocessor">#include &lt;cstdlib&gt;</span></div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a>;</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a>;</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="keyword">class </span>GraphLenetExample : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1utils_1_1_example.xhtml">Example</a></div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;{</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;    <span class="keywordtype">void</span> do_setup(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)<span class="keyword"> override</span></div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;        std::string  data_path;   </div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches = 4; </div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;        <span class="comment">// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON</span></div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span> target      = argc &gt; 1 ? std::strtol(argv[1], <span class="keyword">nullptr</span>, 10) : 0;</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;        <a class="code" href="namespacearm__compute_1_1graph.xhtml#a31488d29805a596498c0234ae392d35d">Target</a>    target_hint = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab6dc388200717b5fae17342af13f5e41">set_target_hint</a>(target);</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;        <a class="code" href="namespacearm__compute_1_1graph.xhtml#ac85a46f3ebd3ab09f576a994ac2dce11">FastMathHint</a> fast_math_hint = FastMathHint::DISABLED;</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;        <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;        <span class="keywordflow">if</span>(argc &lt; 2)</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;        {</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;            <span class="comment">// Print help</span></div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; [target] [path_to_data] [batches] [fast_math_hint]\n\n&quot;</span>;</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;No data folder provided: using random values\n\n&quot;</span>;</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;        }</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;        {</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[1] &lt;&lt; <span class="stringliteral">&quot; [path_to_data] [batches] [fast_math_hint]\n\n&quot;</span>;</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;No data folder provided: using random values\n\n&quot;</span>;</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;        }</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 3)</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;        {</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;            <span class="comment">//Do something with argv[1]</span></div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;            data_path = argv[2];</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; [path_to_data] [batches] [fast_math_hint]\n\n&quot;</span>;</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;No number of batches where specified, thus will use the default : &quot;</span> &lt;&lt; batches &lt;&lt; <span class="stringliteral">&quot;\n\n&quot;</span>;</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;        }</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 4)</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;        {</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;            data_path = argv[2];</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;            batches   = std::strtol(argv[3], <span class="keyword">nullptr</span>, 0);</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[1] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[2] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[3] &lt;&lt; <span class="stringliteral">&quot; [fast_math_hint]\n\n&quot;</span>;</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;No fast math info provided: disabling fast math\n\n&quot;</span>;</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;        }</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;        <span class="keywordflow">else</span></div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;        {</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;            <span class="comment">//Do something with argv[1] and argv[2]</span></div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;            data_path      = argv[2];</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;            batches        = std::strtol(argv[3], <span class="keyword">nullptr</span>, 0);</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;            fast_math_hint = (std::strtol(argv[4], <span class="keyword">nullptr</span>, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;        }</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;        <span class="comment">//conv1 &lt;&lt; pool1 &lt;&lt; conv2 &lt;&lt; pool2 &lt;&lt; fc1 &lt;&lt; act1 &lt;&lt; fc2 &lt;&lt; smx</span></div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;        graph &lt;&lt; target_hint</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;              &lt;&lt; fast_math_hint</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>), <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a9984cc47279cdb732b7b83caf0627de6">get_input_accessor</a>(<span class="stringliteral">&quot;&quot;</span>))</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;                  5U, 5U, 20U,</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;                  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/conv1_w.npy&quot;</span>),</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;                  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/conv1_b.npy&quot;</span>),</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;                  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv1&quot;</span>)</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 2, PadStrideInfo(2, 2, 0, 0))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;pool1&quot;</span>)</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;                  5U, 5U, 50U,</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;                  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/conv2_w.npy&quot;</span>),</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;                  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/conv2_b.npy&quot;</span>),</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;                  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2&quot;</span>)</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 2, PadStrideInfo(2, 2, 0, 0))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;pool2&quot;</span>)</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;                  500U,</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;                  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/ip1_w.npy&quot;</span>),</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;                  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/ip1_b.npy&quot;</span>))</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;ip1&quot;</span>)</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;relu&quot;</span>)</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;                  10U,</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;                  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/ip2_w.npy&quot;</span>),</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;                  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/lenet_model/ip2_b.npy&quot;</span>))</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;ip2&quot;</span>)</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer.xhtml">SoftmaxLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;prob&quot;</span>)</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">OutputLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#aaf0c8eff756108c8bb23aecf51d44f79">get_output_accessor</a>(<span class="stringliteral">&quot;&quot;</span>));</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;        <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;        GraphConfig config;</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;        config.use_tuner = (target == 2);</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;        graph.finalize(target_hint, config);</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    }</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;        <span class="comment">// Run graph</span></div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        graph.run();</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    }</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph{ 0, <span class="stringliteral">&quot;LeNet&quot;</span> };</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;};</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;</div><div class="line"><a name="l00141"></a><span class="lineno"><a class="line" href="graph__lenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">  141</a></span>&#160;<span class="keywordtype">int</span> <a class="code" href="graph__lenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;{</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    <span class="keywordflow">return</span> arm_compute::utils::run_example&lt;GraphLenetExample&gt;(argc, argv);</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">arm_compute::graph::frontend::PoolingLayer</a></div><div class="ttdoc">Pooling Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00336">Layers.h:336</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab6dc388200717b5fae17342af13f5e41"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab6dc388200717b5fae17342af13f5e41">arm_compute::graph_utils::set_target_hint</a></div><div class="ttdeci">graph::Target set_target_hint(int target)</div><div class="ttdoc">Utility function to return the TargetHint. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00370">GraphUtils.h:370</a></div></div>
<div class="ttc" id="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">arm_compute::ActivationLayerInfo::ActivationFunction::RELU</a></div><div class="ttdoc">Rectifier (  ) </div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_aaf0c8eff756108c8bb23aecf51d44f79"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#aaf0c8eff756108c8bb23aecf51d44f79">arm_compute::graph_utils::get_output_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_output_accessor(const std::string &amp;labels_path, size_t top_n=5, std::ostream &amp;output_stream=std::cout)</div><div class="ttdoc">Generates appropriate output accessor according to the specified labels_path. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00330">GraphUtils.h:330</a></div></div>
<div class="ttc" id="utils_2_utils_8h_xhtml"><div class="ttname"><a href="utils_2_utils_8h.xhtml">Utils.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel </div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_input_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">arm_compute::graph::frontend::InputLayer</a></div><div class="ttdoc">Input Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00045">Layers.h:45</a></div></div>
<div class="ttc" id="_graph_utils_8h_xhtml"><div class="ttname"><a href="_graph_utils_8h.xhtml">GraphUtils.h</a></div></div>
<div class="ttc" id="graph_8h_xhtml"><div class="ttname"><a href="graph_8h.xhtml">graph.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_example_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_example.xhtml">arm_compute::utils::Example</a></div><div class="ttdoc">Abstract Example class. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00062">Utils.h:62</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a9984cc47279cdb732b7b83caf0627de6"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a9984cc47279cdb732b7b83caf0627de6">arm_compute::graph_utils::get_input_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_input_accessor(const std::string &amp;ppm_path, std::unique_ptr&lt; IPreprocessor &gt; preprocessor=nullptr, bool bgr=true)</div><div class="ttdoc">Generates appropriate input accessor according to the specified ppm_path. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00299">GraphUtils.h:299</a></div></div>
<div class="ttc" id="graph__lenet_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__lenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for LeNet. </div><div class="ttdef"><b>Definition:</b> <a href="graph__lenet_8cpp_source.xhtml#l00141">graph_lenet.cpp:141</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">arm_compute::graph::frontend::ActivationLayer</a></div><div class="ttdoc">Activation Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00094">Layers.h:94</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a31488d29805a596498c0234ae392d35d"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a31488d29805a596498c0234ae392d35d">arm_compute::graph::Target</a></div><div class="ttdeci">Target</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00084">Types.h:84</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">arm_compute::graph::frontend::ConvolutionLayer</a></div><div class="ttdoc">Convolution Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00158">Layers.h:158</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_ac85a46f3ebd3ab09f576a994ac2dce11"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#ac85a46f3ebd3ab09f576a994ac2dce11">arm_compute::graph::FastMathHint</a></div><div class="ttdeci">FastMathHint</div><div class="ttdoc">Enable or disable fast math for Convolution layer. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00118">Types.h:118</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_cast_8h_source.xhtml#l00031">Cast.h:31</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00041">GraphUtils.h:41</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer.xhtml">arm_compute::graph::frontend::SoftmaxLayer</a></div><div class="ttdoc">Softmax Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00411">Layers.h:411</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_output_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">arm_compute::graph::frontend::OutputLayer</a></div><div class="ttdoc">Output Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00070">Layers.h:70</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer.xhtml">arm_compute::graph::frontend::FullyConnectedLayer</a></div><div class="ttdoc">Fully Connected Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00281">Layers.h:281</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a30bee0b52a919bbcb1dc48b1b6546a16"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">arm_compute::graph_utils::get_weights_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_weights_accessor(const std::string &amp;path, const std::string &amp;data_file, DataLayout file_layout=DataLayout::NCHW)</div><div class="ttdoc">Generates appropriate weights accessor according to the specified path. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00275">GraphUtils.h:275</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_stream_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">arm_compute::graph::frontend::Stream</a></div><div class="ttdoc">Stream frontend class to construct simple graphs in a stream fashion. </div><div class="ttdef"><b>Definition:</b> <a href="_stream_8h_source.xhtml#l00045">Stream.h:45</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1frontend_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00031">ILayer.h:31</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5"><div class="ttname"><a href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">arm_compute::NonLinearFilterFunction::MAX</a></div><div class="ttdoc">Non linear dilate. </div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_layer_xhtml_af664a2598e05f8de28fb9f94e3902886"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">arm_compute::graph::frontend::ILayer::set_name</a></div><div class="ttdeci">ILayer &amp; set_name(std::string name)</div><div class="ttdoc">Sets the name of the layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00055">ILayer.h:55</a></div></div>
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